R codes for the regression analyses
Masatoshi Katabuchi
July 15, 2021
This R markdown file contains the code to reproduce the figures in “Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form” by Chen Ya-Jun et al. in Ecology Letters.
1 Packages
library(tidyverse)
library(rstanarm)
library(bayesplot)
library(tictoc)
library(ggpubr)
library(ggrepel)
library(kableExtra)
#rstan::rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores()) # Run on multiple cores
my_ggsave <- function(plot, filename, height = 11.4, width = 11.4, units = "cm", ...){
ggsave(filename = filename,
plot = plot,
height = height,
width = width,
units = units,
dpi = 600,
...)
}
theme_set(theme_bw())2 Data
This is how the data looks like.
d <- read_csv("data/traits.csv") %>%
mutate(Leafhabit2 = ifelse(Leafhabit == "D", "D", "E_SD"))
d <- d %>%
rename(log10KS = KS) %>%
rename(log10LMA = LMA) %>%
rename(log10rleaf = rleaf) %>%
rename(log10SWCleaf = SWCleaf) %>%
rename(log10epsilon = epsilon) %>%
rename(log10Cft = Cft) %>%
rename(log10WOODSWC = WOODSWC) %>%
mutate(log10KS = log10(log10KS)) %>%
mutate(log10LMA = log10(log10LMA)) %>%
mutate(log10rleaf = log10(log10rleaf)) %>%
mutate(log10SWCleaf = log10(log10SWCleaf)) %>%
mutate(log10epsilon = log10(log10epsilon)) %>%
mutate(log10Cft = log10(log10Cft)) %>%
mutate(log10WOODSWC = log10(log10WOODSWC))
d %>%
head %>%
kable() %>%
#kable_styling("hover")
kable_styling()| Species | Sp | Leafhabit | Lifeform | MVL | log10KS | P50 | P88 | HSM50 | HSM88 | HSMTLP | HV | rxyl | log10WOODSWC | po | ptlp | log10LMA | log10rleaf | Fmin | log10SWCleaf | log10Cft | log10epsilon | RWCtlp | AllBranch | DeadBranch | Branchdiebak | AllTree | DeadTree | Mortality | Leafhabit2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Carissa spinarum | CAS | E | S | 69.83 | 0.1303338 | -4.45 | -8.59 | NA | NA | 1.51 | NA | 0.72 | -0.1307683 | -2.38 | -2.94 | 2.273024 | -0.3467875 | NA | 0.0453230 | -0.4317983 | 1.456062 | 88.89 | NA | NA | NA | NA | NA | NA | E_SD |
| Diospyros yunnanensis | DIY | E | T | 53.40 | -0.2518120 | -6.41 | -9.68 | 2.73 | 6.00 | 3.23 | 2.10 | 0.63 | -0.1870866 | -2.39 | -3.18 | 2.134209 | -0.1135093 | -3.68 | 0.0211893 | -0.3665315 | 1.158664 | 83.25 | 21 | 16 | 76.19 | 114 | 5 | 4.4 | E_SD |
| Jasminum nudiflorum | JAN | E | S | 90.00 | 0.1367206 | -5.62 | -11.94 | 0.07 | 6.39 | 3.15 | 1.77 | 0.74 | -0.2676062 | -2.04 | -2.47 | 2.025101 | -0.1426675 | -5.55 | 0.3117539 | -0.2218487 | 1.235781 | 86.94 | 25 | 13 | 52.00 | 47 | 7 | 14.9 | E_SD |
| Maytenus esquirolii | MAE | E | S | 67.83 | 0.0253059 | -7.34 | -12.34 | NA | NA | 4.52 | 2.45 | 0.72 | -0.1426675 | -2.22 | -2.82 | 2.185882 | -0.2924298 | -4.56 | 0.1430148 | -0.3187588 | 1.304921 | 87.40 | 31 | 9 | 29.03 | NA | NA | NA | E_SD |
| Murraya paniculata? | MUE | E | S | 35.08 | -0.2441251 | -5.80 | -9.35 | NA | NA | 3.12 | NA | 0.83 | -0.3372422 | -2.06 | -2.68 | 2.162594 | NA | NA | 0.0086002 | -0.5086383 | 1.442166 | 90.89 | NA | NA | NA | NA | NA | NA | E_SD |
| Olea ferruginea | OLF | E | T | 62.50 | 0.0413927 | -7.08 | -14.15 | 1.45 | 8.52 | 3.57 | 2.49 | 0.87 | -0.4317983 | -2.98 | -3.51 | 2.251322 | -0.1135093 | -5.63 | 0.0606978 | -0.3665315 | 1.446692 | 85.12 | 14 | 0 | 0.00 | 16 | 0 | 0.0 | E_SD |
3 Outliers
We check outliers first.
dl <- d %>%
pivot_longer(MVL:RWCtlp) %>%
mutate(name2 = case_when(
name == "log10KS" ~ "log[10]~K[S]~(kg~m^{-1}~s^{-1}~MPa^{-1})",
name == "log10Cft" ~ "log[10]~C[leaf]~(mol~m^-2~MPa^-1)",
name == "P50" ~ "P[50]~(MPa)",
name == "P88" ~ "P[88]~(MPa)",
name == "HSM50" ~ "HSM[50]~(MPa)",
name == "HSM88" ~ "HSM[88]~(MPa)",
name == "HSMTLP" ~ "HSM[tlp]~(MPa)",
name == "po" ~ "pi[o]~(MPa)",
name == "ptlp" ~ "pi[tlp]~(MPa)",
name == "HV" ~ "H[V]~(mm^2~cm^-2)",
name == "Fmin" ~ "Psi[min]~(MPa)",
name == "log10epsilon" ~ "log[10]~epsilon~(MPa)",
name == "log10LMA" ~ "log[10]~LMA~(g~m^-2)",
name == "MVL" ~ "MVL~(cm)",
name == "log10SWCleaf" ~ "log[10]~SWC[leaf]~(g~g^-1)",
name == "log10WOODSWC" ~ "log[10]~SWC[wood]~(g~g^-1)",
name == "RWCtlp" ~ "RWC[tlp]~('%')",
name == "log10rleaf" ~ "log[10]~rho[leaf]~(g~cm^-3)",
name == "rxyl" ~ "rho[wood]~(g~cm^-3)",
TRUE ~ name,
))
dl %>%
filter(!is.na(DeadTree)) %>%
ggplot(., aes(x = value, y = DeadTree/AllTree * 100, col = Leafhabit)) +
geom_point() +
geom_text_repel(aes(label = Sp)) +
facet_wrap(~name, scale = "free_x") +
ylab("Top-kill ratio (%)")
dl %>%
filter(!is.na(DeadBranch)) %>%
ggplot(., aes(x = value, y = DeadBranch/AllBranch * 100, col = Leafhabit)) +
geom_point() +
geom_text_repel(aes(label = Sp)) +
facet_wrap(~name, scale = "free_x") +
ylab("Branch dieback ratio (%)")
These seem to be outliers and we remove those sample from the analysis.
| Trait | Species |
|---|---|
| Cft | WOF |
| epslioin | PIW |
| HV | ACFA |
| KS | BACH |
| SWCleaf | ARO |
| WOODSWC | CIR |
| P50 | PIW |
| P88 | PIW |
| HSM88 | PIW |
| HSMTLP | PIW |
dl <- dl %>%
mutate(name2 = factor(name2,
levels = c(
"log[10]~K[S]~(kg~m^{-1}~s^{-1}~MPa^{-1})",
"P[50]~(MPa)",
"P[88]~(MPa)",
"MVL~(cm)",
"H[V]~(mm^2~cm^-2)",
"Psi[min]~(MPa)",
"rho[wood]~(g~cm^-3)",
"log[10]~SWC[wood]~(g~g^-1)",
"pi[o]~(MPa)",
"pi[tlp]~(MPa)",
"log[10]~SWC[leaf]~(g~g^-1)",
"log[10]~C[leaf]~(mol~m^-2~MPa^-1)",
"log[10]~epsilon~(MPa)",
"RWC[tlp]~('%')",
"log[10]~LMA~(g~m^-2)",
"log[10]~rho[leaf]~(g~cm^-3)",
"HSM[50]~(MPa)",
"HSM[88]~(MPa)",
"HSM[tlp]~(MPa)"
)))
dl0 <- dl
dl <- dl %>%
filter(name != "log10Cft" | Sp != "WOF") %>%
filter(name != "log10epsilon" | Sp != "PIW") %>%
filter(name != "HV" | Sp != "ACFA") %>%
filter(name != "log10KS" | Sp != "BACH") %>%
filter(name != "log10SWCleaf" | Sp != "ARO") %>%
filter(name != "log10WOODSWC" | Sp != "CIR") %>%
filter(name != "P50" | Sp != "PIW") %>%
filter(name != "P88" | Sp != "PIW") %>%
filter(name != "HSM88" | Sp != "PIW") %>%
filter(name != "HSMTLP" | Sp != "PIW")
dl_out <- dl0 %>%
filter((name == "log10Cft" & Sp == "WOF") |
(name == "log10epsilon" & Sp == "PIW") |
(name == "HV" & Sp == "ACFA") |
(name == "log10KS" & Sp == "BACH") |
(name == "log10SWCleaf" & Sp == "ARO") |
(name == "log10WOODSWC" & Sp == "CIR") |
(name == "P50" & Sp == "PIW") |
(name == "P88" & Sp == "PIW") |
(name == "HSM88" & Sp == "PIW") |
(name == "HSMTLP" & Sp == "PIW"))4 Model
4.1 Each traits
- We model dieback and top-kill ratios as a function of each trait.
- We remove data that don’t have mortatliy or branch death data.
- We also remove outliers (see above).
set.seed(123)
n_iter <- 2000
n_chain <- 4
my_prior <- normal(location = 0, scale = 10, autoscale = TRUE)
my_prior_aux <- cauchy(location = 0, scale = 2.5, autoscale = FALSE)
tic()
nd <- dl %>%
filter(!is.na(AllBranch)) %>%
group_by(name) %>%
nest() %>%
# head(2) %>%
mutate(m_branch_LH = map(data, ~ stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Leafhabit*value,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = ., family = binomial))) %>%
mutate(m_branch_LH2 = map(data, ~ stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Leafhabit2*value,
iter = n_iter,
chains = n_chain,
refresh = 0,
verbose = FALSE,
prior = my_prior,
prior_intercept = my_prior,
data = ., family = binomial))) %>%
mutate(m_branch_LF = map(data, ~ stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Lifeform*value,
iter = n_iter,
chains = n_chain,
refresh = 0,
verbose = FALSE,
prior = my_prior,
prior_intercept = my_prior,
data = ., family = binomial)))
nd_tree <- dl %>%
filter(!is.na(AllTree)) %>%
group_by(name) %>%
nest() %>%
mutate(m_tree_LH = map(data, ~ stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Leafhabit*value,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = ., family = binomial))) %>%
mutate(m_tree_LH2 = map(data, ~ stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Leafhabit2*value,
iter = n_iter,
chains = n_chain,
refresh = 0,
verbose = FALSE,
prior = my_prior,
prior_intercept = my_prior,
data = ., family = binomial))) %>%
mutate(m_tree_LF = map(data, ~ stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Lifeform*value,
iter = n_iter,
chains = n_chain,
refresh = 0,
verbose = FALSE,
prior = my_prior,
prior_intercept = my_prior,
data = ., family = binomial)))
toc()## 231.267 sec elapsedcheck_fun <- function(posterior) {
if (quantile(posterior, 0.975) > 0 & quantile(posterior, 0.025) > 0) {
"sig"
} else if (quantile(posterior, 0.975) < 0 & quantile(posterior, 0.025) < 0) {
"sig"
} else "NS"}
check_dat_fun <- function(fit, gr) {
pred_mat <- as_tibble(fit)
if (gr == "Leafhabit") {
tibble(Leafhabit = c("D", "E", "SD"),
sig_LH = c(
check_fun(pred_mat$value),
check_fun(pred_mat$value + pred_mat$`LeafhabitE:value`),
check_fun(pred_mat$value + pred_mat$`LeafhabitSD:value`)
))
} else if (gr == "Leafhabit2") {
tibble(Leafhabit2 = c("D", "E_SD"),
sig_LH2 = c(
check_fun(pred_mat$value),
check_fun(pred_mat$value + pred_mat$`Leafhabit2E_SD:value`)
))
} else if (gr == "Lifeform"){
tibble(Lifeform = c("L", "S", "T"),
sig_LF = c(
check_fun(pred_mat$value),
check_fun(pred_mat$value + pred_mat$`LifeformS:value`),
check_fun(pred_mat$value + pred_mat$`LifeformT:value`)
))
}# else {
#
# }
}
logistic <- function(x) 1 / (1 + exp (-x))dat_fun <- function(data, fit, gr = c("LH", "LH2", "LF")) {
data2 <- data %>%
dplyr::select(Leafhabit, Leafhabit2, Lifeform, value) %>%
na.omit
if (gr == "LH") {
new_data <- data2 %>%
group_by(Leafhabit) %>%
summarize(min = min(value), max = max(value)) %>%
group_by(Leafhabit) %>%
nest %>%
mutate(value = map(data, ~ seq(.[1,1] %>% unlist, .[1,2] %>% unlist, length.out = 80))) %>%
dplyr::select(-data) %>%
unnest(cols = c(value))
} else if (gr == "LH2") {
new_data <- data2 %>%
group_by(Leafhabit2) %>%
summarize(min = min(value), max = max(value)) %>%
group_by(Leafhabit2) %>%
nest %>%
mutate(value = map(data, ~ seq(.[1,1] %>% unlist, .[1,2] %>% unlist, length.out = 80))) %>%
dplyr::select(-data) %>%
unnest(cols = c(value))
} else if (gr == "LF") {
new_data <- data2 %>%
group_by(Lifeform) %>%
summarize(min = min(value), max = max(value)) %>%
group_by(Lifeform) %>%
nest %>%
mutate(value = map(data, ~ seq(.[1,1] %>% unlist, .[1,2] %>% unlist, length.out = 80))) %>%
dplyr::select(-data) %>%
unnest(cols = c(value))
}
pred_lin <- posterior_linpred(fit, newdata = new_data)
tibble(
# Leafhabit = new_data$Leafhabit,
# Lifeform = new_data$Lifeform,
median = pred_lin %>% apply(., 2, median),
lower = pred_lin %>% apply(., 2, function(x)quantile(x, 0.025)),
upper = pred_lin %>% apply(., 2, function(x)quantile(x, 0.975))) %>%
bind_cols(new_data, .) %>%
mutate(median_p = logistic(median)) %>%
mutate(lower_p = logistic(lower)) %>%
mutate(upper_p = logistic(upper))
}nd2 <- nd %>%
mutate(dat_branch_LH = map2(data, m_branch_LH, dat_fun, "LH")) %>%
mutate(dat_branch_LH2 = map2(data, m_branch_LH2, dat_fun, "LH2")) %>%
mutate(dat_branch_LF = map2(data, m_branch_LF, dat_fun, "LF"))nd2_tree <- nd_tree %>%
mutate(dat_tree_LH = map2(data, m_tree_LH, dat_fun, "LH")) %>%
mutate(dat_tree_LH2 = map2(data, m_tree_LH2, dat_fun, "LH2")) %>%
mutate(dat_tree_LF = map2(data, m_tree_LF, dat_fun, "LF"))save.image("data/model.Rda")load("data/model.Rda")nd_tmp <- dl %>%
filter(!is.na(AllBranch)) %>%
group_by(name, name2) %>%
nest() %>%
dplyr::select(name, name2) %>%
mutate(name2 = factor(name2,
levels = c(
"log[10]~K[S]~(kg~m^{-1}~s^{-1}~MPa^{-1})",
"P[50]~(MPa)",
"P[88]~(MPa)",
"MVL~(cm)",
"H[V]~(mm^2~cm^-2)",
"Psi[min]~(MPa)",
"rho[wood]~(g~cm^-3)",
"log[10]~SWC[wood]~(g~g^-1)",
"pi[o]~(MPa)",
"pi[tlp]~(MPa)",
"log[10]~SWC[leaf]~(g~g^-1)",
"log[10]~C[leaf]~(mol~m^-2~MPa^-1)",
"log[10]~epsilon~(MPa)",
"RWC[tlp]~('%')",
"log[10]~LMA~(g~m^-2)",
"log[10]~rho[leaf]~(g~cm^-3)",
"HSM[50]~(MPa)",
"HSM[88]~(MPa)",
"HSM[tlp]~(MPa)"
)
))
nd <- left_join(nd, nd_tmp, by = "name")
nd2 <- left_join(nd2, nd_tmp, by = "name")
nd2_tree <- left_join(nd2_tree, nd_tmp, by = "name")4.2 Bayesian R2
Functions to get the Bayesian R2 values.
get_r2 <- function(ypred, y, median = TRUE) {
e <- -1 * sweep(ypred, 2, y)
var_ypred <- apply(ypred, 1, var)
var_e <- apply(e, 1, var)
r2 <- var_ypred / (var_ypred + var_e)
if (median) median(r2) else r2
}
get_r2_fun <- function(fit, trait) {
ypred <- posterior_linpred(fit) %>% logistic
ypred <- ypred * 100
get_r2(ypred, trait)
}Next, we model the branch dieback and top-kill ratios as functions of groups (i.e., leaf habits or growth forms) to estimate the Bayesian R2 for the group effects alone models.
dat_branch <- d %>% filter(!is.na(DeadBranch))
b_fit_LH <- stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Leafhabit,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_branch, family = binomial)
b_fit_LH2 <- stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Leafhabit2,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_branch, family = binomial)
b_fit_LF <- stan_glm(cbind(DeadBranch, AllBranch - DeadBranch) ~
Lifeform,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_branch, family = binomial)
dat_tree <- d %>% filter(!is.na(DeadTree))
t_fit_LH <- stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Leafhabit,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_tree, family = binomial)
t_fit_LH2 <- stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Leafhabit2,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_tree, family = binomial)
t_fit_LF <- stan_glm(cbind(DeadTree, AllTree - DeadTree) ~
Lifeform,
iter = n_iter,
chains = n_chain,
prior = my_prior,
refresh = 0,
verbose = FALSE,
prior_intercept = my_prior,
data = dat_tree, family = binomial)R2 for group effects alone models.
r2_branch_LH_v <- get_r2_fun(b_fit_LH, dat_branch$Branchdiebak)
r2_branch_LH2_v <- get_r2_fun(b_fit_LH2, dat_branch$Branchdiebak)
r2_branch_LF_v <- get_r2_fun(b_fit_LF, dat_branch$Branchdiebak)
r2_tree_LH_v <- get_r2_fun(t_fit_LH, dat_tree$Mortality)
r2_tree_LH2_v <- get_r2_fun(t_fit_LH2, dat_tree$Mortality)
r2_tree_LF_v <- get_r2_fun(t_fit_LF, dat_tree$Mortality)
r2_dat <- tibble(
model = c(
"dieback-Leafhabit (E, SD, L)",
"dieback-Leafhabit (E + SD, L)",
"dieback-Growthform",
"top-kill-Leafhabit (E, SD, L)",
"top-kill-Leafhabit (E + SD, L)",
"top-kill-Growthform"),
r2 = c(
r2_branch_LH_v,
r2_branch_LH2_v,
r2_branch_LF_v,
r2_tree_LH_v,
r2_tree_LH2_v,
r2_tree_LF_v))
r2_dat %>%
mutate(r2 = round(r2, 2)) %>%
kable() %>%
kable_styling()| model | r2 |
|---|---|
| dieback-Leafhabit (E, SD, L) | 0.28 |
| dieback-Leafhabit (E + SD, L) | 0.14 |
| dieback-Growthform | 0.05 |
| top-kill-Leafhabit (E, SD, L) | 0.34 |
| top-kill-Leafhabit (E + SD, L) | 0.20 |
| top-kill-Growthform | 0.41 |
For example, leaf habit alone explains 28% of variance in branch dieback (r2 = 0.28).
r2_branch <- nd2 %>%
mutate(data2 = map(data, ~ filter(., !is.na(value))))%>%
mutate(branch_diebak = map(data2, ~ .$Branchdiebak)) %>%
# mutate(branch_diebak_raw = map(branch_diebak, ~ logit(. /100))) %>%
mutate(pred_branch_LH = map(m_branch_LH, ~ logistic(posterior_linpred(.)) * 100)) %>%
mutate(pred_branch_LH2 = map(m_branch_LH2, ~ logistic(posterior_linpred(.)) * 100)) %>%
mutate(pred_branch_LF = map(m_branch_LF, ~ logistic(posterior_linpred(.)) * 100)) %>%
# mutate(pred_branch_LH_raw = map(m_branch_LH, ~ posterior_linpred(.))) %>%
mutate(r2_branch_LH = map2_dbl(pred_branch_LH, branch_diebak, get_r2)) %>%
mutate(r2_branch_LH2 = map2_dbl(pred_branch_LH2, branch_diebak, get_r2)) %>%
mutate(r2_branch_LF = map2_dbl(pred_branch_LF, branch_diebak, get_r2)) %>%
# mutate(r2_branch_LH_raw = map2_dbl(pred_branch_LH_raw, branch_diebak_raw, get_r2)) %>%
#dplyr::select(name, r2_branch_LH, r2_branch_LH_raw) %>%
dplyr::select(name, r2_branch_LH, r2_branch_LH2, r2_branch_LF)# %>%
#arrange(desc(r2_branch_LH))
tibble(trait = r2_branch$name, round(r2_branch[,2:4], 2)) %>%
rename(`3 Leaf habit` = "r2_branch_LH") %>%
rename(`2 Leaf habit` = "r2_branch_LH2") %>%
rename(`Growth form` = "r2_branch_LF") %>%
kable() %>%
add_header_above(c(" ", "R2 values" = 3)) %>%
kable_styling()| trait | 3 Leaf habit | 2 Leaf habit | Growth form |
|---|---|---|---|
| MVL | 0.32 | 0.18 | 0.22 |
| log10KS | 0.40 | 0.21 | 0.24 |
| P50 | 0.58 | 0.57 | 0.41 |
| P88 | 0.68 | 0.64 | 0.34 |
| HSM50 | 0.41 | 0.25 | 0.31 |
| HSM88 | 0.70 | 0.57 | 0.44 |
| HSMTLP | 0.63 | 0.60 | 0.47 |
| HV | 0.25 | 0.16 | 0.15 |
| rxyl | 0.41 | 0.25 | 0.15 |
| log10WOODSWC | 0.34 | 0.19 | 0.18 |
| po | 0.50 | 0.46 | 0.41 |
| ptlp | 0.52 | 0.51 | 0.50 |
| log10LMA | 0.45 | 0.29 | 0.26 |
| log10rleaf | 0.39 | 0.23 | 0.18 |
| Fmin | 0.34 | 0.25 | 0.19 |
| log10SWCleaf | 0.45 | 0.34 | 0.22 |
| log10Cft | 0.48 | 0.32 | 0.21 |
| log10epsilon | 0.59 | 0.42 | 0.36 |
| RWCtlp | 0.47 | 0.36 | 0.34 |
r2_tree <- nd2_tree %>%
mutate(data2 = map(data, ~ filter(., !is.na(value))))%>%
mutate(mort = map(data2, ~ .$Mortality)) %>%
mutate(pred_tree_LH = map(m_tree_LH, ~ logistic(posterior_linpred(.)) * 100)) %>%
mutate(pred_tree_LH2 = map(m_tree_LH2, ~ logistic(posterior_linpred(.)) * 100)) %>%
mutate(pred_tree_LF = map(m_tree_LF, ~ logistic(posterior_linpred(.)) * 100)) %>%
mutate(r2_tree_LH = map2_dbl(pred_tree_LH, mort, get_r2)) %>%
mutate(r2_tree_LH2 = map2_dbl(pred_tree_LH2, mort, get_r2)) %>%
mutate(r2_tree_LF = map2_dbl(pred_tree_LF, mort, get_r2)) %>%
dplyr::select(name, r2_tree_LH, r2_tree_LH2, r2_tree_LF)# %>%
#arrange(desc(r2_tree_LH))
tibble(trait = r2_tree$name, round(r2_tree[,2:4], 2)) %>%
rename(`3 Leaf habit` = "r2_tree_LH") %>%
rename(`2 Leaf habit` = "r2_tree_LH2") %>%
rename(`Growth form` = "r2_tree_LF") %>%
kable() %>%
add_header_above(c(" ", "R2 values" = 3)) %>%
kable_styling()| trait | 3 Leaf habit | 2 Leaf habit | Growth form |
|---|---|---|---|
| MVL | 0.39 | 0.22 | 0.41 |
| log10KS | 0.42 | 0.18 | 0.38 |
| P50 | 0.69 | 0.65 | 0.66 |
| P88 | 0.77 | 0.72 | 0.73 |
| HSM50 | 0.50 | 0.36 | 0.44 |
| HSM88 | 0.65 | 0.55 | 0.57 |
| HSMTLP | 0.63 | 0.56 | 0.62 |
| HV | 0.39 | 0.21 | 0.42 |
| rxyl | 0.48 | 0.26 | 0.41 |
| log10WOODSWC | 0.35 | 0.19 | 0.64 |
| po | 0.70 | 0.70 | 0.69 |
| ptlp | 0.75 | 0.74 | 0.75 |
| log10LMA | 0.36 | 0.20 | 0.44 |
| log10rleaf | 0.75 | 0.39 | 0.74 |
| Fmin | 0.42 | 0.34 | 0.70 |
| log10SWCleaf | 0.39 | 0.28 | 0.48 |
| log10Cft | 0.74 | 0.63 | 0.61 |
| log10epsilon | 0.86 | 0.86 | 0.90 |
| RWCtlp | 0.82 | 0.79 | 0.89 |
5 Figures
5.1 Color code
- Evergreen: green “#008B00”
- Deciduous: orange “#FF8C00”
- Semi-deciduous: blue “#1874CD”
- Tree: light blue “#61f0f8”
- Liana: red “#de2e19”
- Shrub: dark blue “#1f306f”
gr <- "#008B00"
or <- "#FF8C00"
bl <- "#1874CD"
br <- "#8B7355"
lbl <- "#61F0F8"
re <- "#DE2E19"
dbl <- "#1F306F"
my_col <- c(gr, or, bl, lbl, re, dbl)
expand.grid(x = 1:3, y = 1:2) %>%
mutate(gr = c("Evergreen", "Deciduous", "Semi-diciduous", "Tree", "Liana",
"Shurb")) %>%
mutate(my_col = my_col) %>%
ggplot(aes(x, y, fill = I(my_col))) +
geom_tile() +
annotate("text", x = 1:3, y = 1, label = c("Evergreen", "Deciduous", "Semi-deciduous")) +
annotate("text", x = 1:3, y = 2, label = c("Tree", "Liana", "Shrub"), size = 4) +
coord_equal() +
theme_void() +
scale_y_reverse()
LF_shapes <- c("Liana"=16, "Shrub"=17, "Tree"=15)
LF_shapes2 <- c("L"=21, "S"=22, "T"=24)
LH_shapes <- c("D"=16, "E"=17, "SD"=15)5.2 Branch dieback ratio and 3 leaf habits
sig_LH <- nd2 %>%
mutate(sig_LH = map(m_branch_LH, check_dat_fun, "Leafhabit")) %>%
# mutate(sig_LF= map(m_branch_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LH) %>%
unnest(c( sig_LH))
sig_LH2 <- nd2 %>%
mutate(sig_LH2 = map(m_branch_LH2, check_dat_fun, "Leafhabit2")) %>%
# mutate(sig_LF= map(m_branch_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LH2) %>%
unnest(c( sig_LH2))
sig_LF <- nd2 %>%
mutate(sig_LF = map(m_branch_LF, check_dat_fun, "Lifeform")) %>%
# mutate(sig_LF= map(m_branch_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LF) %>%
unnest(c( sig_LF))
r2_branch2 <- r2_branch %>%
left_join(., nd2, by = "name") %>%
dplyr::select(name, r2_branch_LH, r2_branch_LH2, r2_branch_LF, data) %>%
mutate(min_x = map_dbl(data, ~ min(.$value, na.rm = TRUE))) %>%
mutate(max_x = map_dbl(data, ~ max(.$value, na.rm = TRUE))) %>%
#dplyr::select(name, r2_branch_LH, data) %>%
unnest(cols = data) %>%
dplyr::select(name, name2, min_x, max_x, r2_branch_LH, r2_branch_LH2, r2_branch_LF) %>%
unique %>%
mutate(Leafhabit = "D") %>%
mutate(Leafhabit2 = "D") %>%
mutate(Lifeform = "T") %>%
mutate(r2_branch_LF = round(r2_branch_LF, 2) %>% format(., nsmall = 2)) %>%
mutate(r2_branch_LH2 = round(r2_branch_LH2, 2) %>% format(., nsmall = 2)) %>%
mutate(r2_branch_LH = round(r2_branch_LH, 2) %>% format(., nsmall = 2)) %>%
mutate(LH_lab = paste0("italic(r^2) == ", r2_branch_LH)) %>%
mutate(LH2_lab = paste0("italic(r^2) == ", r2_branch_LH2)) %>%
mutate(LF_lab = paste0("italic(r^2) == ", r2_branch_LF))
p_B_LH_R2 <- nd2 %>%
dplyr::select(name2, dat_branch_LH) %>%
unnest(c(dat_branch_LH)) %>%
ungroup %>%
left_join(., sig_LH, by = c("Leafhabit", "name")) %>%
filter(sig_LH == "sig") %>%
ggplot(., aes(x = value, col = Leafhabit, shape = Leafhabit)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Leafhabit), alpha = 0.5, col = "transparent",
show.legend = FALSE) +
geom_point(data = dl %>% filter(!is.na(AllBranch)), aes(x = value, y = DeadBranch/AllBranch * 100)) +
geom_point(data = dl_out %>% filter(!is.na(AllBranch)),
aes(y = DeadBranch/AllBranch * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_branch2,
aes(y = 110, x = min_x, label = LH_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
scale_linetype_manual(values = c(2, 1)) +
scale_shape_manual(values = LH_shapes,
labels = c("Deciduous", "Evergreen", "Semi-deciduous")) +
scale_colour_manual(values = c(or, gr, bl),
labels = c("Deciduous", "Evergreen", "Semi-deciduous")) +
scale_fill_manual(values = c(or, gr, bl)) +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
ylab("Branch dieback ratio (%)") +
xlab("") +
labs(color = "Leaf habit", shape = "Leaf habit") +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_B_LH_R2, "figs/BD_LH_R2.png", height = 20, width = 30)
my_ggsave(p_B_LH_R2, "figs/BD_LH_R2.pdf", height = 20, width = 30)
5.3 Branch dieback ratio and 2 leaf habits
p_B_LH2_R2 <- nd2 %>%
dplyr::select(name2, dat_branch_LH2) %>%
unnest(c(dat_branch_LH2)) %>%
ungroup %>%
left_join(., sig_LH2, by = c("Leafhabit2", "name")) %>%
filter(sig_LH2 == "sig") %>%
ggplot(., aes(x = value, col = Leafhabit2, shape = Leafhabit2)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Leafhabit2), alpha = 0.5, col = "transparent",
show.legend = FALSE) +
geom_point(data = dl %>% filter(!is.na(AllBranch)), aes(x = value, y = DeadBranch/AllBranch * 100)) +
geom_point(data = dl_out %>% filter(!is.na(AllBranch)),
aes(y = DeadBranch/AllBranch * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_branch2,
aes(y = 110, x = min_x, label = LH2_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
scale_linetype_manual(values = c(2, 1)) +
scale_shape_manual(values = c(16, 17),
labels = c("Deciduous", "Evergreen and \nSemi-deciduous")) +
scale_colour_manual(values = c(or, gr),
labels = c("Deciduous", "Evergreen and \nSemi-deciduous")) +
scale_fill_manual(values = c(or, gr)) +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
ylab("Branch dieback ratio (%)") +
xlab("") +
labs(color = "Leaf habit", shape = "Leaf habit") +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_B_LH2_R2, "figs/BD_LH2_R2.png", height = 20, width = 30)
my_ggsave(p_B_LH2_R2, "figs/BD_LH2_R2.pdf", height = 20, width = 30)
5.4 Branch dieback ratio and growth forms
p_B_LF_R2 <- nd2 %>%
dplyr::select(name2, dat_branch_LF) %>%
unnest(c(dat_branch_LF)) %>%
ungroup %>%
left_join(., sig_LF, by = c("Lifeform", "name")) %>%
filter(sig_LF == "sig") %>%
ggplot(., aes(x = value, col = Lifeform, fill = Lifeform)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Lifeform), alpha = 0.8, col = "transparent",
show.legend = FALSE) +
geom_point(data = dl %>% filter(!is.na(AllBranch)), aes(x = value, y = DeadBranch/AllBranch * 100, shape = Lifeform), col = "black") +
geom_point(data = dl_out %>% filter(!is.na(AllBranch)),
aes(y = DeadBranch/AllBranch * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_branch2,
aes(y = 110, x = min_x, label = LF_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
#scale_linetype_manual(values = c(2, 1)) +
scale_shape_manual(values = LF_shapes2,
labels = c("Liana", "Shrub", "Tree")) +
scale_colour_manual(values = c(re, dbl, lbl
#)) +
), guide = "none") +
scale_fill_manual(values = c(re, dbl, lbl),
labels = c("Liana", "Shrub", "Tree")
) +
ylab("Branch dieback ratio (%)") +
xlab("") +
labs(fill = "Growth form", shape = "Growth form", col = "Growth form") +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_B_LF_R2, "figs/BD_LF_R2.png", height = 20, width = 30)
my_ggsave(p_B_LF_R2, "figs/BD_LF_R2.pdf", height = 20, width = 30)
5.5 Top-kill ratio and 3 leaf habits
sig_LH <- nd2_tree %>%
mutate(sig_LH = map(m_tree_LH, check_dat_fun, "Leafhabit")) %>%
# mutate(sig_LF= map(m_tree_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LH) %>%
unnest(c( sig_LH))
sig_LH2 <- nd2_tree %>%
mutate(sig_LH2 = map(m_tree_LH2, check_dat_fun, "Leafhabit2")) %>%
# mutate(sig_LF= map(m_tree_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LH2) %>%
unnest(c( sig_LH2))
sig_LF <- nd2_tree %>%
mutate(sig_LF = map(m_tree_LF, check_dat_fun, "Lifeform")) %>%
# mutate(sig_LF= map(m_tree_LF, check_dat_fun, "Lifeform")) %>%
dplyr::select(sig_LF) %>%
unnest(c( sig_LF))
r2_tree2 <- r2_tree %>%
left_join(., nd2, by = "name") %>%
dplyr::select(name, r2_tree_LH, r2_tree_LH2, r2_tree_LF, data) %>%
mutate(min_x = map_dbl(data, ~ min(.$value, na.rm = TRUE))) %>%
mutate(max_x = map_dbl(data, ~ max(.$value, na.rm = TRUE))) %>%
#dplyr::select(name, r2_tree_LH, data) %>%
unnest(cols = data) %>%
dplyr::select(name, name2, min_x, max_x, r2_tree_LH, r2_tree_LH2, r2_tree_LF) %>%
unique %>%
mutate(Leafhabit = "D") %>%
mutate(Leafhabit2 = "D") %>%
mutate(Lifeform = "T") %>%
mutate(r2_tree_LF = round(r2_tree_LF, 2) %>% format(., nsmall = 2)) %>%
mutate(r2_tree_LH2 = round(r2_tree_LH2, 2) %>% format(., nsmall = 2)) %>%
mutate(r2_tree_LH = round(r2_tree_LH, 2) %>% format(., nsmall = 2)) %>%
mutate(LH_lab = paste0("italic(r^2) == ", r2_tree_LH)) %>%
mutate(LH2_lab = paste0("italic(r^2) == ", r2_tree_LH2)) %>%
mutate(LF_lab = paste0("italic(r^2) == ", r2_tree_LF))
p_T_LH_R2 <- nd2_tree %>%
dplyr::select(name2, dat_tree_LH) %>%
unnest(c(dat_tree_LH)) %>%
ungroup %>%
left_join(., sig_LH, by = c("Leafhabit", "name")) %>%
filter(sig_LH == "sig") %>%
ggplot(., aes(x = value, col = Leafhabit, shape = Leafhabit)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Leafhabit), alpha = 0.5, col = "transparent",
show.legend = FALSE) +
geom_point(data = dl %>% filter(!is.na(AllTree)), aes(x = value, y = DeadTree/AllTree * 100)) +
geom_point(data = dl_out %>% filter(!is.na(AllTree)),
aes(y = DeadTree/AllTree * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_tree2,
aes(y = 110, x = min_x, label = LH_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
scale_linetype_manual(values = c(2, 1)) +
scale_shape_manual(values = LH_shapes,
labels = c("Deciduous", "Evergreen", "Semi-deciduous")) +
scale_colour_manual(values = c(or, gr, bl),
labels = c("Deciduous", "Evergreen", "Semi-deciduous")) +
scale_fill_manual(values = c(or, gr, bl)) +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
ylab("Top-kill ratio (%)") +
xlab("") +
labs(color = "Leaf habit", shape = "Leaf habit") +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_T_LH_R2, "figs/TD_LH_R2.png", height = 20, width = 30)
my_ggsave(p_T_LH_R2, "figs/TD_LH_R2.pdf", height = 20, width = 30)
5.6 Top-kill ratio and 2 leaf habits
p_T_LH2_R2 <- nd2_tree %>%
dplyr::select(name2, dat_tree_LH2) %>%
unnest(c(dat_tree_LH2)) %>%
ungroup %>%
left_join(., sig_LH2, by = c("Leafhabit2", "name")) %>%
filter(sig_LH2 == "sig") %>%
ggplot(., aes(x = value, col = Leafhabit2, shape = Leafhabit2)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Leafhabit2), alpha = 0.5, col = "transparent", show.legend
= FALSE) +
geom_point(data = dl %>% filter(!is.na(AllTree)), aes(x = value, y = DeadTree/AllTree * 100)) +
geom_point(data = dl_out %>% filter(!is.na(AllTree)),
aes(y = DeadTree/AllTree * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_tree2,
aes(y = 110, x = min_x, label = LH_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
scale_linetype_manual(values = c(2, 1)) +
scale_shape_manual(values = c(16, 17),
labels = c("Deciduous", "Evergreen and \nSemi-deciduous")) +
scale_colour_manual(values = c(or, gr),
labels = c("Deciduous", "Evergreen and \nSemi-deciduous")) +
scale_fill_manual(values = c(or, gr
)) +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
ylab("Top-kill ratio (%)") +
xlab("") +
labs(color = "Leaf habit", shape = "Leaf habit") +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_T_LH2_R2, "figs/TD_LH2_R2.png", height = 20, width = 25)
my_ggsave(p_T_LH2_R2, "figs/TD_LH2_R2.pdf", height = 20, width = 25)
5.7 Top-kill ratio and growth forms
p_T_LF_R2 <- nd2_tree %>%
dplyr::select(name2, dat_tree_LF) %>%
unnest(c(dat_tree_LF)) %>%
ungroup %>%
left_join(., sig_LF, by = c("Lifeform", "name")) %>%
filter(sig_LF == "sig") %>%
ggplot(., aes(x = value, col = Lifeform, fill = Lifeform)) +
geom_ribbon(aes(ymin = lower_p * 100, ymax = upper_p * 100, fill =
Lifeform), alpha = 0.8, col = "transparent",
show.legend = FALSE) +
geom_point(data = dl %>% filter(!is.na(AllTree)),
aes(x = value, y = DeadTree/AllTree * 100,
shape = Lifeform),
col = "black"
) +
geom_point(data = dl_out %>% filter(!is.na(AllTree)),
aes(y = DeadTree/AllTree * 100), fill = "grey", col = "black", pch = 21) +
geom_line(aes(y = median_p * 100)) +
geom_text(data = r2_tree2,
aes(y = 110, x = min_x, label = LH_lab),
hjust = -0.2,
parse = TRUE,
col = "black"
) +
facet_wrap(~name2, scale = "free_x", label = label_parsed) +
scale_shape_manual(values = LF_shapes2,
labels = c("Liana", "Shrub", "Tree")) +
scale_colour_manual(values = c(re, dbl, lbl
), guide = "none") +
scale_fill_manual(values = c(re, dbl, lbl),
labels = c("Liana", "Shrub", "Tree")
) +
labs(fill = "Growth form") +
scale_y_continuous(breaks = seq(0, 100, length = 5),
limits = c(0, 115)) +
labs(fill = "Growth form", shape = "Growth form", col = "Growth form") +
ylab("Top-kill ratio (%)") +
xlab("") +
theme(legend.position = c(0.9, 0.1))
my_ggsave(p_T_LF_R2, "figs/TD_LF_R2.png", height = 20, width = 30)
my_ggsave(p_T_LF_R2, "figs/TD_LF_R2.pdf", height = 20, width = 30)
6 Computing Environment
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.5 (2021-03-31)
## os Ubuntu 20.04.2 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-07-15
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.0.3)
## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.3)
## backports 1.2.1 2020-12-09 [1] RSPM (R 4.0.3)
## base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.0.3)
## bayesplot * 1.8.0 2021-01-10 [1] RSPM (R 4.0.3)
## boot 1.3-27 2021-02-12 [2] CRAN (R 4.0.5)
## broom 0.7.6 2021-04-05 [1] RSPM (R 4.0.4)
## bslib 0.2.5.1 2021-05-18 [1] RSPM (R 4.0.5)
## cachem 1.0.5 2021-05-15 [1] RSPM (R 4.0.4)
## callr 3.6.0 2021-03-28 [1] RSPM (R 4.0.4)
## car 3.0-10 2020-09-29 [1] RSPM (R 4.0.3)
## carData 3.0-4 2020-05-22 [1] RSPM (R 4.0.3)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.0.3)
## cli 3.0.0 2021-06-30 [1] RSPM (R 4.0.5)
## codetools 0.2-18 2020-11-04 [2] CRAN (R 4.0.5)
## colorspace 2.0-0 2020-11-11 [1] RSPM (R 4.0.3)
## colourpicker 1.1.0 2020-09-14 [1] RSPM (R 4.0.2)
## crayon 1.4.1 2021-02-08 [1] RSPM (R 4.0.3)
## crosstalk 1.1.1 2021-01-12 [1] RSPM (R 4.0.3)
## curl 4.3 2019-12-02 [1] RSPM (R 4.0.3)
## data.table 1.14.0 2021-02-21 [1] RSPM (R 4.0.3)
## DBI 1.1.1 2021-01-15 [1] RSPM (R 4.0.3)
## dbplyr 2.1.1 2021-04-06 [1] RSPM (R 4.0.4)
## desc 1.3.0 2021-03-05 [1] RSPM (R 4.0.3)
## devtools 2.4.0 2021-04-07 [1] RSPM (R 4.0.4)
## digest 0.6.27 2020-10-24 [1] RSPM (R 4.0.3)
## dplyr * 1.0.7 2021-06-18 [1] RSPM (R 4.0.5)
## DT 0.18 2021-04-14 [1] RSPM (R 4.0.4)
## dygraphs 1.1.1.6 2018-07-11 [1] RSPM (R 4.0.3)
## ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.0.4)
## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.3)
## fansi 0.5.0 2021-05-25 [1] RSPM (R 4.0.5)
## farver 2.1.0 2021-02-28 [1] RSPM (R 4.0.3)
## fastmap 1.1.0 2021-01-25 [1] RSPM (R 4.0.3)
## forcats * 0.5.1 2021-01-27 [1] RSPM (R 4.0.3)
## foreign 0.8-81 2020-12-22 [2] CRAN (R 4.0.5)
## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3)
## generics 0.1.0 2020-10-31 [1] RSPM (R 4.0.3)
## ggplot2 * 3.3.3 2020-12-30 [1] RSPM (R 4.0.3)
## ggpubr * 0.4.0 2020-06-27 [1] RSPM (R 4.0.3)
## ggrepel * 0.9.1 2021-01-15 [1] RSPM (R 4.0.3)
## ggridges 0.5.3 2021-01-08 [1] RSPM (R 4.0.3)
## ggsignif 0.6.1 2021-02-23 [1] RSPM (R 4.0.3)
## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.3)
## gridExtra 2.3 2017-09-09 [1] RSPM (R 4.0.3)
## gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.3)
## gtools 3.8.2 2020-03-31 [1] RSPM (R 4.0.3)
## haven 2.4.1 2021-04-23 [1] RSPM (R 4.0.5)
## highr 0.9 2021-04-16 [1] RSPM (R 4.0.4)
## hms 1.1.0 2021-05-17 [1] RSPM (R 4.0.4)
## htmltools 0.5.1.1 2021-01-22 [1] RSPM (R 4.0.3)
## htmlwidgets 1.5.3 2020-12-10 [1] RSPM (R 4.0.3)
## httpuv 1.6.1 2021-05-07 [1] RSPM (R 4.0.5)
## httr 1.4.2 2020-07-20 [1] RSPM (R 4.0.3)
## igraph 1.2.6 2020-10-06 [1] RSPM (R 4.0.3)
## inline 0.3.17 2020-12-01 [1] RSPM (R 4.0.3)
## jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.0.4)
## jsonlite 1.7.2 2020-12-09 [1] RSPM (R 4.0.3)
## kableExtra * 1.3.4 2021-02-20 [1] RSPM (R 4.0.3)
## knitr 1.33 2021-04-24 [1] RSPM (R 4.0.4)
## labeling 0.4.2 2020-10-20 [1] RSPM (R 4.0.3)
## later 1.2.0 2021-04-23 [1] RSPM (R 4.0.4)
## lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.5)
## lifecycle 1.0.0 2021-02-15 [1] RSPM (R 4.0.3)
## lme4 1.1-26 2020-12-01 [1] RSPM (R 4.0.3)
## loo 2.4.1 2020-12-09 [1] RSPM (R 4.0.3)
## lubridate 1.7.10 2021-02-26 [1] RSPM (R 4.0.3)
## magrittr 2.0.1 2020-11-17 [1] RSPM (R 4.0.3)
## markdown 1.1 2019-08-07 [1] RSPM (R 4.0.3)
## MASS 7.3-53.1 2021-02-12 [2] CRAN (R 4.0.5)
## Matrix 1.3-2 2021-01-06 [2] CRAN (R 4.0.5)
## matrixStats 0.58.0 2021-01-29 [1] RSPM (R 4.0.3)
## memoise 2.0.0 2021-01-26 [1] RSPM (R 4.0.3)
## mime 0.11 2021-06-23 [1] RSPM (R 4.0.5)
## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.0.3)
## minqa 1.2.4 2014-10-09 [1] RSPM (R 4.0.3)
## modelr 0.1.8 2020-05-19 [1] RSPM (R 4.0.3)
## munsell 0.5.0 2018-06-12 [1] RSPM (R 4.0.3)
## nlme 3.1-152 2021-02-04 [2] CRAN (R 4.0.5)
## nloptr 1.2.2.2 2020-07-02 [1] RSPM (R 4.0.3)
## openxlsx 4.2.3 2020-10-27 [1] RSPM (R 4.0.3)
## pillar 1.6.1 2021-05-16 [1] RSPM (R 4.0.4)
## pkgbuild 1.2.0 2020-12-15 [1] RSPM (R 4.0.3)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3)
## pkgload 1.2.1 2021-04-06 [1] RSPM (R 4.0.4)
## plyr 1.8.6 2020-03-03 [1] RSPM (R 4.0.3)
## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3)
## processx 3.5.1 2021-04-04 [1] RSPM (R 4.0.4)
## promises 1.2.0.1 2021-02-11 [1] RSPM (R 4.0.3)
## ps 1.6.0 2021-02-28 [1] RSPM (R 4.0.3)
## purrr * 0.3.4 2020-04-17 [1] RSPM (R 4.0.3)
## R6 2.5.0 2020-10-28 [1] RSPM (R 4.0.3)
## Rcpp * 1.0.7 2021-07-07 [1] RSPM (R 4.0.5)
## RcppParallel 5.1.2 2021-04-15 [1] RSPM (R 4.0.4)
## readr * 1.4.0 2020-10-05 [1] RSPM (R 4.0.4)
## readxl 1.3.1 2019-03-13 [1] RSPM (R 4.0.3)
## remotes 2.3.0 2021-04-01 [1] RSPM (R 4.0.4)
## reprex 2.0.0 2021-04-02 [1] RSPM (R 4.0.4)
## reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.0.3)
## rio 0.5.26 2021-03-01 [1] RSPM (R 4.0.3)
## rlang 0.4.11 2021-04-30 [1] RSPM (R 4.0.4)
## rmarkdown 2.7 2021-02-19 [1] RSPM (R 4.0.3)
## rprojroot 2.0.2 2020-11-15 [1] RSPM (R 4.0.3)
## rsconnect 0.8.17 2021-04-09 [1] RSPM (R 4.0.4)
## rstan 2.21.2 2020-07-27 [1] RSPM (R 4.0.4)
## rstanarm * 2.21.1 2020-07-20 [1] RSPM (R 4.0.4)
## rstantools 2.1.1 2020-07-06 [1] RSPM (R 4.0.3)
## rstatix 0.7.0 2021-02-13 [1] RSPM (R 4.0.3)
## rstudioapi 0.13 2020-11-12 [1] RSPM (R 4.0.3)
## rvest 1.0.0 2021-03-09 [1] RSPM (R 4.0.3)
## sass 0.4.0 2021-05-12 [1] RSPM (R 4.0.4)
## scales 1.1.1 2020-05-11 [1] RSPM (R 4.0.3)
## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3)
## shiny 1.6.0 2021-01-25 [1] RSPM (R 4.0.3)
## shinyjs 2.0.0 2020-09-09 [1] RSPM (R 4.0.3)
## shinystan 2.5.0 2018-05-01 [1] RSPM (R 4.0.0)
## shinythemes 1.2.0 2021-01-25 [1] RSPM (R 4.0.3)
## StanHeaders 2.21.0-7 2020-12-17 [1] RSPM (R 4.0.4)
## statmod 1.4.35 2020-10-19 [1] RSPM (R 4.0.3)
## stringi 1.6.2 2021-05-17 [1] RSPM (R 4.0.4)
## stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.0.3)
## survival 3.2-10 2021-03-16 [2] CRAN (R 4.0.5)
## svglite 2.0.0 2021-02-20 [1] RSPM (R 4.0.4)
## systemfonts 1.0.1 2021-02-09 [1] RSPM (R 4.0.4)
## testthat 3.0.2 2021-02-14 [1] RSPM (R 4.0.3)
## threejs 0.3.3 2020-01-21 [1] RSPM (R 4.0.0)
## tibble * 3.1.2 2021-05-16 [1] RSPM (R 4.0.4)
## tictoc * 1.0.1 2021-04-19 [1] RSPM (R 4.0.4)
## tidyr * 1.1.3 2021-03-03 [1] RSPM (R 4.0.4)
## tidyselect 1.1.1 2021-04-30 [1] RSPM (R 4.0.4)
## tidyverse * 1.3.1 2021-04-15 [1] RSPM (R 4.0.4)
## usethis 2.0.1 2021-02-10 [1] RSPM (R 4.0.3)
## utf8 1.2.1 2021-03-12 [1] RSPM (R 4.0.3)
## V8 3.4.1 2021-04-23 [1] RSPM (R 4.0.4)
## vctrs 0.3.8 2021-04-29 [1] RSPM (R 4.0.4)
## viridisLite 0.4.0 2021-04-13 [1] RSPM (R 4.0.4)
## webshot 0.5.2 2019-11-22 [1] RSPM (R 4.0.3)
## withr 2.4.2 2021-04-18 [1] RSPM (R 4.0.4)
## xfun 0.24 2021-06-15 [1] RSPM (R 4.0.5)
## xml2 1.3.2 2020-04-23 [1] RSPM (R 4.0.3)
## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.0.3)
## xts 0.12.1 2020-09-09 [1] RSPM (R 4.0.3)
## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3)
## zip 2.1.1 2020-08-27 [1] RSPM (R 4.0.3)
## zoo 1.8-9 2021-03-09 [1] RSPM (R 4.0.3)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library
## [3] /home/mattocci/R/x86_64-pc-linux-gnu-library/4.0